https://orcid.org/0000-0002-9905-111X
References
- Castle J, Hendry D, Kitov O. Forecasting and nowcasting macroeconomic variables: a methodological overview. Economics series working paper 674, University of Oxford, Department of Economics; 2013.
- Foroni C, Marcellino M, Schumacher C. Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. J Roy Stat Soc Ser A. 2015;178(1):57–82.
- Ghysels E, Santa-Clara P, Valkanov R. The MIDAS touch: mixed data sampling regression models. Working paper, UNC and UCLA; 2002.
- Ghysels E, Santa-Clara P, Valkanov R. Predicting volatility: getting the most out of return data sampled at different frequencies. Anderson School of Management working paper and UNC Department of Economics working paper; 2003.
- Ghysels E, Santa-Clara P, Valkanov R. Predicting volatility: getting the most out of return data sampled at different frequencies. J. Econom. 2006;131(1):59–95.
- Bai J, Ghysels E, Wright JH. State space models and MIDAS regressions. Econom Rev. 2013;32(7):779–813.
- Clements MP, Galvão AB. Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J Bus Econ Stat. 2008;26(4):546–554.
- Clements MP, Galvão AB. Forecasting US output growth using leading indicators: an appraisal using MIDAS models. J Appl Econom. 2009;24(7):1187–1206.
- Duarte C, Rodrigues PMM, Rua A. A mixed frequency approach to the forecasting of private consumption with ATM/POS data. Int J Forecast. 2017;33(1):61–75.
- Ghysels E, Sinko A, Valkanov R. MIDAS regressions: further results and new directions. Econom Rev. 2007;26(1):53–90.
- Schumacher C. MIDAS regressions with time-varying parameters: an application to corporate bond spreads and GDP in the euro area. In Annual conference 2014 (Hamburg): Evidence-based economic policy, Verein für Socialpolitik/German Economic Association; 2014.
- Box GEP, Jenkins GM. Time series analysis: forecasting and control. San Francisco: Holden-Day; 1976.
- Ghysels E. Matlab toolbox for mixed sampling frequency data analysis using MIDAS regression models. Technical report; 2014.
- Foroni C, Marcellino MG. A survey of econometric methods for mixed-frequency data. Norges Bank working paper; 2013.
- Foroni C, Marcellino M, Schumacher C. U-MIDAS: MIDAS regressions with unrestricted lag polynomials. CEPR discussion paper 8828; 2012.
- Ghysels E, Valkanov R. Linear time series processes with mixed data sampling and MIDAS regression models. Mimeo; 2006. Available from: http://ssrn.com/abstract=920610.
- Garcia-Hiernaux A, Casals J, Jerez M. Fast estimation methods for time-series models in state-space form. J Stat Comput Simul. 2009;79(2):121–134.
- Casals J, Garcia-Hiernaux A, Jerez M, et al. State-space methods for time series analysis: theory, applications and software. Boca Raton: Chapman & Hall; 2016.
- Ghysels E, and collaborators. MIDAS Matlab Toolbox. Version 2.2. MATLAB Central File Exchange; 2017. Available from: https://la.mathworks.com/matlabcentral/fileexchange/45150-midas-matlabtoolbox.
- Ghysels E, Santa-Clara P, Valkanov R. There is a risk-return trade-off after all. J Financ Econ. 2005;76(3):509–548.